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Does Alcohol Increase the Risk of Preterm Delivery?

2001· article· en· W2424543488 on OpenAlexaboutno aff
Ulrik Schiøler Kesmodel, Niels Jørgen Secher, Sjúrđur F. Olsen

Bibliographic record

VenueEpidemiology · 2001
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWineUnit of alcoholAlcoholBottleAlcohol intakeQuarter (Canadian coin)Environmental healthDemographyAlcohol consumptionFood scienceGeography

Abstract

fetched live from OpenAlex

The authors respond: Kilduff et al suggest that underreporting is more likely in a questionnaire for the medical record than in a research questionnaire. For the period 1 February 2000 through 31 December 2000 we have collected both types of information: In the questionnaire for the medical record (QMR) we asked a single question comparable with that that used for the analyses of preterm delivery 1 (“How many drinks do you approximately drink per week now that you are pregnant (one drink is the equivalent of one [bottle of] beer, one glass of wine, or one schnapps)?”). The question did not specify subcategories of alcohol, and possible answers were 0; <1 drink/week; any whole number of drinks/week: 1, 2 etc. In the research questionnaire (QRES) we asked about average weekly intake of beer, wine, fortified wine and spirits, including strength of beer, and alcohol free beer and wine (subsequently coded as 0). Possible answers for each subtype of alcohol were as above. Intake of <1 drink/week was coded as a quarter of a drink/week. A total of 4,546 women returned QMR, of whom 4,411 had answered the question on alcohol intake, and 4,030 had filled in QRES. For 3969 women information was available for both instruments. Mean difference between the two measures (QRES − QMR) was 0.1 drinks/week (standard deviation, SD = 0.4). Eighty-six percent of women reported the same intake in both questionnaires, 5% underreported, and 10% overreported intake in QRES compared with QMR (Table 1). Interestingly, the tendency toward underreporting in QMR compared with QRES was most evident at the lowest intake levels, and might be explained by the more detailed questioning in QRES. Further, women who had not filled in QRES were more likely to be abstainers (61% versus 46% as measured in QMR), and smokers (21% versus 13%) compared with women who had filled in QRES. So, in this case, one would have to weigh what little may possibly be gained by using information from QRES against this selection bias. Table 1: Agreement Between Two Measures of Alcohol Intake During Pregnancy (Drinks/Week): Questionnaire for the Medical Record (QMR) Versus Research Questionnaire (QRES)Comparing the data from the questionnaire for the medical record with information from a more extensive interview, where the same precategorized answers were used as those reported earlier, 1 69% of women reported the same intake, 23% underreported their intake in the questionnaire compared with the interview (95% within one category), and 8% overreported (86% within one category). 2 In a later study we found that mean intake was 0.4 (SD = 1.2) drinks/week lower in the questionnaire compared with a two-week diary, and 0.3 (0.9) drinks/week lower compared with an average measure from an interview. 3 With respect to information on smoking habits, measurement error of potential confounders may distort the results. 4 We have previously compared the prospectively collected information on smoking habits with retrospectively collected information from questionnaires and found no noteworthy differences. 5 Differences were independent of recall time and pregnancy outcome, including preterm delivery (mean difference between methods (current − retrospective): 0.17 cigarettes/day (−0.32, 0.65) for preterm versus term deliveries). 5 Interestingly, recall diminished with increasing alcohol intake, particularly for women smoking ≥10 cigarettes/day. 5 It may be that both measures were underreported compared with interviews. We have recently collected data that may shed light on this point (data not yet available for analyses). Alternatively, measurements of cotinine in saliva, 6,7 serum, 8 or urine, 9 or of carbon monoxide in expired air 10 may be used as measures of smoking habits. It seems, however, that pregnant women claiming to be non-smokers may have high cotinine levels in serum and urine 8,11 (possibly because of exposure to passive smoking or denial of smoking status), and vice versa. 8,11 The findings of smokers with low cotinine levels suggest that because of intraindividual differences in cotinine concentrations in body fluids, a combination of self-reports and biological markers would be preferable. We take this opportunity to note that there was a minor error on page 513, left column, last paragraph, third sentence in the original article. 1 The definition of a drink is the equivalent to 4 cL (centiliters) of spirits, not 4 mL as stated in the original. Ulrik Kesmodel Niels Jørgen Secher Sjúrđur Fróđi Olsen

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.307
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2001
Admission routes1
Has abstractyes

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